This research focuses on the analysis of Floating Car Data (FCD) data to understand sustainable transportation behavior in the area of Amsterdam. Using data collected by mobile devices, trips are analyzed by their distance and transportation method. Sustainability is a relevant factor to the social and scientific community. With increasing population and growing cities, the impact of travel on the environment is also increasing. New policies are required to stimulate a more sustainable approach to transportation. Research on sustainable travel behavior provides input for the policy makers. The main research question is defined by what extent FCD can be used to provide insights in the sustainable mobility behavior in Amsterdam. In the existing literature, there are four objectives for sustainable mobility: hazards reduction, travel reduction, modal shift and accessibility. The modal shift (e.g. replacing care usage by public transport) is one of the key drivers for this research. To quantify the behavior, different sustainable mobility indicators are identified to determine the sustainable direction related to the mode of transportation. In the literature there are many methods to quantify sustainable transportation behavior ranging from the traditional methods like counting and surveys to modern approaches including smartphone and sensor data. The methodology used starts with the problem statement and literature review. The data sets are selected and analyzed, providing the results and conclusions. A decision tree is used for categorizing different trips people make, where a difference between short and longer distance trips is made. This research makes use of a wide range of tools ranging from PostgreSQL databases to advanced features of ESRI to visualize data. Five different available data sets are analyzed for their suitability for this research. Based on several requirements like availability, having Origin and Destination (OD) information, usability and documentation, the data sets are assessed and two data sets (Google OD and LMS) are chosen to be analyzed in more detail. The data is filtered and cleaned to make sure it fits the scope (Amsterdam) and the two sets are compared to each other. The trips are split into short and long distance trips, where for both a detailed analysis is performed on the trips which can be easily replaced by more sustainable trips. Regarding the short distance trips, analysis shows that walking and biking are the most common option in busy areas as the city center and the business district. The less sustainable car trips for short distances show several patterns in the city. Using interactive maps, these patterns are identified and both data sets are compared. For longer distance car trips, train transportation is the more sustainable option. There is however a tradeoff between the most sustainable and least time-consuming transportation option. This makes that train trips are not always the most logical or even sustainable choice for transportation. A method is developed and applied to the data set to test if a train trip is a realistic alternative. The results show for both data sets that there is a significant amount of car trips which could easily be replaced by the train. During the analysis of the data for long distances, a problem has been found with the Google data set. It seems that this data set is showing most of the time the same noise. This phenomenon was found by comparing the different modes of transportation available in the Google data set. The error is not in all the data, but it seems to have an impact on the data set. Conclusion is that the data provides many insights on travel behavior and different available data sets can be linked together to provide deeper insights. The Google data set shows interesting results for shorter distances but gives less reliable results for longer distances. The LMS data set is used to compare the results for both short and long distances. The use of FCD data to study and stimulate sustainable mobility behavior seems very promising, but the quality of the available data sets has a large influence on the usability.